We compute \(\text{FID}_g\)  on the ground truth AIST++ dataset following the implementation in Li et al. [1]}.
Specifically, we compute metrics on the test set against the training set and obtain
a score of 41.4, a stark increase from metrics obtained from generated outputs: running on FACT outputs gives 12.75, running on Bailando outputs gives 24.82, and running on our final model's outputs gives 23.08.
Given that FACT performs the best in “FID\(_g\) " but performs the worst according to user studies and that all three models perform significantly better than ground truth, we conclude that the “FID\(_g\) " values are unreliable as a measure of quality on this dataset.
